The idea of reinforcement learning--or learning based on reward--has been around for so long it's easy to forget we don't really know how it works. If DeepMind's new bombshell paper in Nature is any indication, a common approach in AI, one that's led to humanity's defeat in the game of Go against machines, may have the answer. We all subconsciously learn complex behaviors in response to positive and negative feedback, but how that works in the brain remains a century-long mystery. By examining a powerful variant of reinforcement learning, dubbed distributional reinforcement learning, that outperforms original methods, the team suggests that the brain may simultaneously represent multiple predicted futures in parallel. Each future is assigned a different probability, or chance of actually occurring, based on reward.

"In #distributional #Reinforcement_Learning, the #AI algorithm predicts a full spectrum of future rewards: some are more optimistic and amplify their reward signals when the reward is larger than expected; others more pessimistic, lowering their reward signals when it's smaller than predicted." "Partnering with Harvard, the teams tested out their idea in the brains of mice. In contrast to neuroscience canon, the team said, reward neurons didn't act as one. Rather than collectively encoding for a single expected outcome, they were each "tuned" to a different prediction, with some expecting a larger amount of reward, and others less hopeful, predicting smaller volumes" "We found that reward neurons in the brain were each tuned to different levels of pessimism or optimism. If they were a choir, they wouldn't all be singing the same note, but harmonizing" "In other words, they seemed to operate on very similar principles to distributed reinforcement learning, a powerful method in #AI." https://lnkd.in/grTTXeA